Cumulative Plot Sales

Column

Plots Sold Across the World

Row

Plot Locations

Plots Sold in the US

World Plots

Column

Cumulative Plot Sales

country sold
United States 1932
United Kingdom 175
France 107
China 55
Italy 55
Japan 54
United Arab Emirates 47
Netherlands 46
Spain 44
Germany 39
Egypt 38
Australia 35
Brazil 32
Uzbekistan 31
Canada 29
India 26
Russia 26
Hong Kong 24
Israel 22
Vatican City 21
Monaco 18
Mexico 17
Panama 15
Saudi Arabia 14
Peru 13
Turkey 12
Ukraine 12
Austria 11
Greece 11
South Korea 11
Switzerland 10
Singapore 9
Thailand 9
Belgium 7
Czech Republic 5
Poland 5
Portugal 5
Iraq 4
Jordan 4
NA 4
coordinates 3
Finland 3
Georgia 3
Iceland 3
Kazakhstan 3
Malaysia 3
Saint Lucia 3
South Africa 3
Taiwan 3
Venezuela 3
Argentina 2
Armenia 2
Azerbaijan 2
Bahamas 2
Cambodia 2
Colombia 2
Croatia 2
Cuba 2
French Polynesia 2
Indonesia 2
Nepal 2
Ontario 2
Bulgaria 1
Chad 1
Chile 1
Cyprus 1
Estonia 1
Guatemala 1
Ireland 1
Jamaica 1
Jerusalem District 1
Luxembourg 1
Malta 1
Montenegro 1
Norway 1
Saint Barthelemy 1
Senegal 1
Serbia 1
Vietnam 1

Column

Day

Week

Month

Year

Total

US Plots

Column

Cumulative US Plot Sales

state sold
Texas 508
New York 329
California 279
Florida 180
Nevada 162
Tennessee 49
District of Columbia 47
Washington 35
Massachusetts 32
Illinois 30
Minnesota 28
Georgia 26
Pennsylvania 24
Ohio 23
Louisiana 22
New Jersey 21
Arizona 18
Indiana 17
Colorado 14
Michigan 13
Missouri 12
Oregon 10
Utah 9
Wisconsin 8
Hawaii 7
Virginia 5
Kentucky 4
Maryland 4
North Carolina 4
South Dakota 3
Alabama 2
New Mexico 2
Wyoming 2
Connecticut 1
Kansas 1
Oklahoma 1

Column

Day

Week

Month

Year

Total

---
title: "SuperWorld Plot Sales"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
    theme: yeti
---

Cumulative Plot Sales
=====================================

Inputs {.sidebar}
-------------------------------------

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
library(leaflet)
library(leaflet.extras)
library(sf)
library(tidyverse)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)
library(usmap)
library(lubridate)

plots_sold = read_csv("C:/Users/rebec/SuperWorld_Plot_Recommendation/data/plots_sold.csv")[-1]
plots_sold$code = toupper(plots_sold$code)

us_plots = plots_sold[which(plots_sold$code == "US"),]
us_address = us_plots$address

state = c()
for (i in 1:length(us_address)){
  add = tail(unlist(str_split(us_address[i], pattern = ", ")), 2)[1]
  add = gsub(' [[:digit:]]+', '', add)
  state = c(state, add)
}

us_plots = cbind(us_plots, state) 

state_data = data.frame(state) %>%
  group_by(state) %>%
  summarise(sold = n())

```

*Total Plot Sales:*

```{r}
nrow(plots_sold)
```


*Top 10 Countries:* ```{r} plots_sold %>% group_by(country) %>% summarise(`plots sold` = n()) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ```
*Top 10 US States:* ```{r} state_data %>% summarise(state, `plots sold` = sold) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ``` Column {data-width=800} ------------------------------------- ### Plots Sold Across the World ```{r warning=FALSE, message=FALSE} world = ne_countries(scale = "medium", returnclass = "sf") df = st_sf(merge(plots_sold, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) df_plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradient(trans = "log") + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) # df2 = df %>% # group_by(country, code) %>% # summarise(sold = n()) %>% # mutate(sold = ifelse(is.na(country), 0, sold)) # plot(df2["sold"], logz = TRUE, main = NULL, key.pos = 4) ggplotly(df_plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` Row ------------------------------------- ### Plot Locations ```{r} leaflet(plots_sold) %>% addTiles() %>% addCircles(lng = ~lon, lat = ~lat) %>% setView(lat = 37.0902, lng = -95.7129, zoom = 4) ``` ### Plots Sold in the US ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_continuous(name = "Plots Sold") ggplotly(us) ``` World Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative Plot Sales ```{r} plots_sold %>% group_by(country) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} plots_today = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) df_today = st_sf(merge(plots_today, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_today %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE)) ``` ### Week ```{r} plots_week = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) df_week = st_sf(merge(plots_week, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_week %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Month ```{r} plots_month = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) df_month = st_sf(merge(plots_month, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_month %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Year ```{r} plots_year = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) df_year = st_sf(merge(plots_year, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_year %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(5, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Total ```{r} plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` US Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative US Plot Sales ```{r} us_plots %>% group_by(state) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} us_today = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) %>% group_by(state) %>% summarise(sold = n()) us_today = plot_usmap(data = us_today, values = "sold", regions = "states") + theme(legend.position = "right") + # scale_fill_continuous(name = "Plots Sold") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_today) ``` ### Week ```{r} us_week = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) %>% group_by(state) %>% summarise(sold = n()) us_week = plot_usmap(data = us_week, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_week) ``` ### Month ```{r} us_month = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) %>% group_by(state) %>% summarise(sold = n()) us_month = plot_usmap(data = us_month, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_month) ``` ### Year ```{r} us_year = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) %>% group_by(state) %>% summarise(sold = n()) us_year = plot_usmap(data = us_year, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_year) ``` ### Total ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us) ```